论文标题
最佳匿名独立奖励计划设计
Optimal Anonymous Independent Reward Scheme Design
论文作者
论文摘要
我们考虑设计奖励方案，以激励代理商创建高质量的内容（例如，视频，图像，文本，想法）。问题是现实世界应用程序的中心，该应用程序的目标是优化用户生成的内容平台上生成的内容的整体质量。我们专注于匿名独立奖励计划（AIRS），这些计划仅将代理商内容的质量作为输入。我们证明总体问题是NP-HARD。如果成本函数是凸，我们表明最佳空气可以作为凸优化问题配制，并提出了一种有效的算法来解决它。接下来，我们探讨最佳线性奖励方案，并证明其具有1/2的附属能力比，并且比率很紧。最后，我们表明比例方案与AIRS相比可能是任意糟糕的。
We consider designing reward schemes that incentivize agents to create high-quality content (e.g., videos, images, text, ideas). The problem is at the center of a real-world application where the goal is to optimize the overall quality of generated content on user-generated content platforms. We focus on anonymous independent reward schemes (AIRS) that only take the quality of an agent's content as input. We prove the general problem is NP-hard. If the cost function is convex, we show the optimal AIRS can be formulated as a convex optimization problem and propose an efficient algorithm to solve it. Next, we explore the optimal linear reward scheme and prove it has a 1/2-approximation ratio, and the ratio is tight. Lastly, we show the proportional scheme can be arbitrarily bad compared to AIRS.
